Zone Preference in the Visual Water Maze
June 26, 2025
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Living organisms require biomolecules for several biological processes like energy storage and regulation of their metabolic cycles. Among all, carbohydrates, nucleic acids, lipids, and proteins are the four major biomolecules (or macromolecules) that are mainly involved in these biological processes.
The functions of carbohydrates are essential for life in all organisms, from microorganisms to plants and humans. They are central to our nutrition and are present in our daily diet in several forms, including in table sugar, milk, honey, fruits, cereals, and vegetables like potatoes.
Carbohydrates were the last molecule among the four macromolecules to get the attention of scientists for research and further explorations. The in-depth study on these molecules enriched the molecular chemistry of biomolecules by introducing the concepts of change in their shape and conformations during a biochemical reaction. Studies on carbohydrates have contributed to a better understanding of biosynthetic reactions, enzymatic control mechanisms, and many fundamental processes.
This article brings you all about the definition, classification, and functions of carbohydrates in different organisms.
Carbohydrates are defined as biomolecules containing a group of naturally occurring carbonyl compounds (aldehydes or ketones) and several hydroxyl groups. It consists of carbon (C), hydrogen (H), and oxygen (O) atoms, usually with a hydrogen-oxygen atom ratio of 2:1 (as in water). It’s represented with the empirical formula Cm(H2O)n (where m and n may or may not be different) or (CH2O)n.
But some compounds do not follow this precise stoichiometric definition, such as uronic acids. And there are others that, despite having groups similar to carbohydrates, are not classified as one of them, e.g., formaldehyde and acetic acid.
Carbohydrates are divided into four major groups based on the degree of polymerization: monosaccharides, disaccharides, oligosaccharides, and polysaccharides. Given below is a brief account of the structure and functions of carbohydrate groups.
Monosaccharides are the simplest carbohydrates and cannot be hydrolyzed into other smaller carbohydrates. The “mono” in monosaccharides means one, which shows the presence of only one sugar unit.
They are the building blocks of disaccharides and polysaccharides. For this reason, they are also known as simple sugars. These simple sugars are colorless, crystalline solids that are soluble in water and insoluble in a nonpolar solvent.
The general formula representing monosaccharide structure is Cn(H2O)n or CnH2nOn. Dihydroxyacetone and D- and L-glyceraldehydes are the smallest monosaccharides – here, n=3.
The monosaccharides containing the aldehyde group (the functional group with the structure, R-CHO) are known as aldolases and the one containing ketone groups is called ketoses (the functional group with the structure RC(=O)R′). Some examples of monosaccharides are glucose, fructose, erythrulose, and ribulose.
D-glucose is the most common, widely distributed, and abundant carbohydrate. It’s commonly known as dextrose and it’s an aldehyde containing six carbon atoms, called aldohexose. It’s present in both, open-chain and cyclic structures.
Most monosaccharide names end with the suffix -ose. And based on the number of carbons, which typically ranges from three to seven, they may be known as trioses (three carbons), tetroses (four carbons), pentoses (five carbons), hexoses (six carbons), and heptoses (seven carbons).
Although glucose, galactose, and fructose all have the chemical formula of C6H12O6, they differ at the structural and chemical levels because of the different arrangement of functional groups around their asymmetric carbon.
Figure: A structural representation of glucose, fructose, and galactose.
Credit: Lumenlearning
Monosaccharides are either present as linear chains or ring-shaped molecules. In a ring form, glucose’s hydroxyl group (-OH) can have two different arrangements around the anomeric carbon (carbon-1 that becomes asymmetric in the process of ring formation).
If the hydroxyl group is below carbon number 1 in the sugar, it is said to be in the alpha (α) position, and if it is above the plane, it is said to be in the beta (β) position.
Figure: A structural representation of ring forms of glucose and fructose.
Credit: Lumenlearning
Disaccharides consist of two sugar units. When subjected to a dehydration reaction (condensation reaction or dehydration synthesis), they release two monosaccharide units.
In this process, the hydroxyl group of one monosaccharide combines with the hydrogen of another monosaccharide through a covalent bond, releasing a molecule of water. The covalent bond formed between the two sugar molecules is known as a glycosidic bond.
The glycosidic bond or glycosidic linkage can be alpha or beta type. The alpha bond is formed when the OH group on the carbon-1 of the first glucose is below the ring plane, and a beta bond is formed when the OH group on the carbon-1 is above the ring plane.
Image: The structural diagram of the process of glycosidic bond formation between two sugar units (glucose and fructose) forming a disaccharide (sucrose).
Credit: Lumenlearning
Some examples of disaccharides are lactose, maltose, and sucrose. Sucrose is the most abundant disaccharide of all and is composed of one D-glucose molecule and one D-fructose molecule. The systematic name for sucrose is O-α-D-glucopyranosyl-(1→2)-D-fructofuranoside.
Lactose occurs naturally in mammalian milk and is composed of one D-galactose molecule and one D-glucose molecule. The systematic name for lactose is O-β-D-galactopyranosyl-(1→4)-D-glucopyranose.
Disaccharides can be classified into two groups based on their ability to undergo oxidation-reduction reactions.
Some other examples of disaccharides include lactulose, chitobiose, kojibiose, nigerose, isomaltose, sophorose, laminaribiose, gentiobiose, turanose, maltulose, trehalose, palatinose, gentiobiulose, mannobiose, melibiose, melibiulose, rutinose, rutinulose, and xylobiose.
A list of disaccharides with their monomer units is given below:
| Disaccharide | Monomer Units |
|---|---|
|
Sucrose |
Glucose and Fructose |
|
Lactose |
Galactose and Glucose |
|
Maltose |
Glucose and Glucose (alpha-1,4 linkage) |
|
Trehalose |
Glucose and Glucose (alpha-1, alpha-1 linkage) |
|
Cellobiose |
Glucose and Glucose (beta-1,4 linkage) |
|
Gentiobiose |
Glucose and Glucose (beta-1,6 linkage) |
Oligosaccharides are compounds that yield 3 to 10 molecules of the same or different monosaccharides on hydrolysis. All the monosaccharides are joined through glycosidic linkage. And based on the number of monosaccharides attached, the oligosaccharides are classified as trisaccharides, tetrasaccharides, pentasaccharides, and so on.
The general formula of trisaccharides is Cn(H2O)n-2, and that of tetrasaccharides is Cn(H2O)n-3, and so on. The oligosaccharides are normally present as glycans. They are linked to either lipids or amino acid side chains in proteins by N- or O-glycosidic bonds known as glycolipids or glycoproteins.
The glycosidic bonds are formed in the process of glycosylation, in which a carbohydrate is covalently attached to an organic molecule, creating structures such as glycoproteins and glycolipids.
Polysaccharides are a chain of more than 10 carbohydrates joined together through glycosidic bond formation. They are ubiquitous and mainly involved in the structural or storage functions of organisms. They are also known as glycans.
These compounds’ physical and biological properties depend on the components & the architecture of their binding or reacting molecules and their interaction with the enzymatic machinery.
Polysaccharides are classified based on their functions, the type of monosaccharide units they contain, or their origin.
Based on the type of monosaccharides involved in the formation of polysaccharide structures, they are classified into two groups: homopolysaccharides and heteropolysaccharides.
They are composed of repeating units of only one type of monomer. A few examples of homopolysaccharides include cellulose, chitin, starches (amylose and amylopectin), glycogen, and xylans. And based on their functional roles, these compounds are classified into structural polysaccharides and storage polysaccharides.
Figure: A structural representation of cellulose.
Credit: Lumenlearning
They are composed of two or more repeating units of different types of monomers. Examples include glycosaminoglycans, agarose, and peptidoglycans. In natural systems, they are linked to proteins, lipids, and peptides.
| GAGs | Acidic sugar | Amino sugar |
|---|---|---|
|
Hyaluronic acid |
D-Glucuronic acid |
N-acetylglucosamine |
|
Chondroitin sulfate |
D-Glucuronic acid |
N-acetylgalactosamine |
|
Heparan sulfate |
D-Glucuronic acid or L-iduronic acid |
N-acetylglucosamine |
|
Heparin |
D-Glucuronic acid or L-iduronic acid |
N-acetylglucosamine |
|
Dermatan sulfate |
D-Glucuronic acid or L-iduronic acid |
N-acetylgalactosamine |
|
Keratan sulfate |
D-Galactose |
N-acetylglucosamine |
Figure: A classification summary of polysaccharides into different sub-groups.
Credit: Brainkart
Figure: Classification summary and examples of carbohydrates.
Credit: Microbenotes
Carbohydrates are one of the four major essential biomolecules required by living organisms. Organisms consume them in several forms, and they are classified into four groups based on the number of monomer units their structure has. They include monosaccharides, disaccharides, oligosaccharides, and polysaccharides.
All carbohydrates contain molecules like glucose, fructose, cellulose, starch, glycoproteins, and chitin which are involved in several organismal functions. Their functions range from providing energy to the cells, supporting the structural integrity of cells, to supporting the organism’s growth and development.
Carbohydrate research has provided scientists with critical insights into conformational changes, molecular kinetics, and much more. And it still has several functions waiting to be discovered by scientists dedicated to studying these molecules.
In behavioral neuroscience, the Open Field Test (OFT) remains one of the most widely used assays to evaluate rodent models of affect, cognition, and motivation. It provides a non-invasive framework for examining how animals respond to novelty, stress, and pharmacological or environmental manipulations. Among the test’s core metrics, the percentage of time spent in the center zone offers a uniquely normalized and sensitive measure of an animal’s emotional reactivity and willingness to engage with a potentially risky environment.
This metric is calculated as the proportion of time spent in the central area of the arena—typically the inner 25%—relative to the entire session duration. By normalizing this value, researchers gain a behaviorally informative variable that is resilient to fluctuations in session length or overall movement levels. This makes it especially valuable in comparative analyses, longitudinal monitoring, and cross-model validation.
Unlike raw center duration, which can be affected by trial design inconsistencies, the percentage-based measure enables clearer comparisons across animals, treatments, and conditions. It plays a key role in identifying trait anxiety, avoidance behavior, risk-taking tendencies, and environmental adaptation, making it indispensable in both basic and translational research contexts.
Whereas simple center duration provides absolute time, the percentage-based metric introduces greater interpretability and reproducibility, especially when comparing different animal models, treatment conditions, or experimental setups. It is particularly effective for quantifying avoidance behaviors, risk assessment strategies, and trait anxiety profiles in both acute and longitudinal designs.
This metric reflects the relative amount of time an animal chooses to spend in the open, exposed portion of the arena—typically defined as the inner 25% of a square or circular enclosure. Because rodents innately prefer the periphery (thigmotaxis), time in the center is inversely associated with anxiety-like behavior. As such, this percentage is considered a sensitive, normalized index of:
Critically, because this metric is normalized by session duration, it accommodates variability in activity levels or testing conditions. This makes it especially suitable for comparing across individuals, treatment groups, or timepoints in longitudinal studies.
A high percentage of center time indicates reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance. reduced anxiety, increased novelty-seeking, or pharmacological modulation (e.g., anxiolysis). Conversely, a low percentage suggests emotional inhibition, behavioral avoidance, or contextual hypervigilance.
The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity, where animals exhibit persistent avoidance of the center due to heightened emotional reactivity. This metric can also distinguish between acute anxiety responses and enduring trait anxiety, especially in longitudinal or developmental studies. Its normalized nature makes it ideal for comparing across cohorts with variable locomotor profiles, helping researchers detect true affective changes rather than activity-based confounds.
Rodents that spend more time in the center zone typically exhibit broader and more flexible exploration strategies. This behavior reflects not only reduced anxiety but also cognitive engagement and environmental curiosity. High center percentage is associated with robust spatial learning, attentional scanning, and memory encoding functions, supported by coordinated activation in the prefrontal cortex, hippocampus, and basal forebrain. In contrast, reduced center engagement may signal spatial rigidity, attentional narrowing, or cognitive withdrawal, particularly in models of neurodegeneration or aging.
The open field test remains one of the most widely accepted platforms for testing anxiolytic and psychotropic drugs. The percentage of center time reliably increases following administration of anxiolytic agents such as benzodiazepines, SSRIs, and GABA-A receptor agonists. This metric serves as a sensitive and reproducible endpoint in preclinical dose-finding studies, mechanistic pharmacology, and compound screening pipelines. It also aids in differentiating true anxiolytic effects from sedation or motor suppression by integrating with other behavioral parameters like distance traveled and entry count (Prut & Belzung, 2003).
Sex-based differences in emotional regulation often manifest in open field behavior, with female rodents generally exhibiting higher variability in center zone metrics due to hormonal cycling. For example, estrogen has been shown to facilitate exploratory behavior and increase center occupancy, while progesterone and stress-induced corticosterone often reduce it. Studies involving gonadectomy, hormone replacement, or sex-specific genetic knockouts use this metric to quantify the impact of endocrine factors on anxiety and exploratory behavior. As such, it remains a vital tool for dissecting sex-dependent neurobehavioral dynamics.
The percentage of center time is one of the most direct, unconditioned readouts of anxiety-like behavior in rodents. It is frequently reduced in models of PTSD, chronic stress, or early-life adversity. Because it is normalized, this metric is especially helpful for distinguishing between genuine avoidance and low general activity.
Environmental Control: Uniformity in environmental conditions is essential. Lighting should be evenly diffused to avoid shadow bias, and noise should be minimized to prevent stress-induced variability. The arena must be cleaned between trials using odor-neutral solutions to eliminate scent trails or pheromone cues that may affect zone preference. Any variation in these conditions can introduce systematic bias in center zone behavior. Use consistent definitions of the center zone (commonly 25% of total area) to allow valid comparisons. Software-based segmentation enhances spatial precision.
Evaluating how center time evolves across the duration of a session—divided into early, middle, and late thirds—provides insight into behavioral transitions and adaptive responses. Animals may begin by avoiding the center, only to gradually increase center time as they habituate to the environment. Conversely, persistently low center time across the session can signal prolonged anxiety, fear generalization, or a trait-like avoidance phenotype.
To validate the significance of center time percentage, it should be examined alongside results from other anxiety-related tests such as the Elevated Plus Maze, Light-Dark Box, or Novelty Suppressed Feeding. Concordance across paradigms supports the reliability of center time as a trait marker, while discordance may indicate task-specific reactivity or behavioral dissociation.
When paired with high-resolution scoring of behavioral events such as rearing, grooming, defecation, or immobility, center time offers a richer view of the animal’s internal state. For example, an animal that spends substantial time in the center while grooming may be coping with mild stress, while another that remains immobile in the periphery may be experiencing more severe anxiety. Microstructure analysis aids in decoding the complexity behind spatial behavior.
Animals naturally vary in their exploratory style. By analyzing percentage of center time across subjects, researchers can identify behavioral subgroups—such as consistently bold individuals who frequently explore the center versus cautious animals that remain along the periphery. These classifications can be used to examine predictors of drug response, resilience to stress, or vulnerability to neuropsychiatric disorders.
In studies with large cohorts or multiple behavioral variables, machine learning techniques such as hierarchical clustering or principal component analysis can incorporate center time percentage to discover novel phenotypic groupings. These data-driven approaches help uncover latent dimensions of behavior that may not be visible through univariate analyses alone.
Total locomotion helps contextualize center time. Low percentage values in animals with minimal movement may reflect sedation or fatigue, while similar values in high-mobility subjects suggest deliberate avoidance. This metric helps distinguish emotional versus motor causes of low center engagement.
This measure indicates how often the animal initiates exploration of the center zone. When combined with percentage of time, it differentiates between frequent but brief visits (indicative of anxiety or impulsivity) versus fewer but sustained center engagements (suggesting comfort and behavioral confidence).
The delay before the first center entry reflects initial threat appraisal. Longer latencies may be associated with heightened fear or low motivation, while shorter latencies are typically linked to exploratory drive or low anxiety.
Time spent hugging the walls offers a spatial counterbalance to center metrics. High thigmotaxis and low center time jointly support an interpretation of strong avoidance behavior. This inverse relationship helps triangulate affective and motivational states.
By expressing center zone activity as a proportion of total trial time, researchers gain a metric that is resistant to session variability and more readily comparable across time, treatment, and model conditions. This normalized measure enhances reproducibility and statistical power, particularly in multi-cohort or cross-laboratory designs.
For experimental designs aimed at assessing anxiety, exploratory strategy, or affective state, the percentage of time spent in the center offers one of the most robust and interpretable measures available in the Open Field Test.
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